Sammenlign metoder

	ETS: Feil, Trend, Sesongglatting	Holt-Winters trippel eksponentiell glatting	Prophet
Fagfelt	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	2008	1960	2018
Opphavsperson	Hyndman, Koehler, Ord & Snyder (state space framework)	Charles C. Holt and Peter R. Winters	Taylor & Letham (Facebook/Meta)
Type	Exponential smoothing state space model	Exponential smoothing forecasting model	Decomposable (structural) time series model
Opprinnelig kilde	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.
Alias	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale
Relaterte	5	4	5
Sammendrag	ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.	Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series.	Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale.